5 year CPE Tranmission - Gene Intersection across species by chromosomes and plasmids

Ran the below commands in Unix shell

cd /data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis


#---------- Identity plasmids and chromosomes using PlasClass

time for j in $(ls *.fasta); do echo $j; time python3.7 ~/sw/PlasClass/classify_fasta.py -f $j -o "$j".plasclass.probs.out -p 60;  done
real    464m57.242s
user    592m31.525s
sys 133m29.767s

mkdir ../1_plasClass_Probs
mv *.plasclass.probs.out ../1_plasClass_Probs
$ awk '{print FILENAME "\t" $0}' *.out >CPE_Trans_PlasClass_Predictions_comb.tab



#---------- Identify Transposons using Bacant (Integron module is not working properly and Resistance genes are used from AMRfinderplus)
time for d in $(ls *.fasta);  do  
echo "$d";
bacant -n "$d" -o "$d"_bacant_out &
done

mkdir ../2_bacant_annot
mv *_bacant_out ../2_bacant_annot

awk '{$13=$14=""; print FILENAME "\t" $1 "\t" $2}' *_bacant_out/transposon.filter.tsv | fgrep -v 'QUERY' | sed 's/_bacant_out\/transposon.filter.tsv//g' | sed -e 's/_circ[0-9]*/_circ/g' -e 's/\(depth_[0-9]*.[0-9]*x\)[0-9]*/\1/g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >CPE_Trans_BacAnt_Transposons.tab 

awk '{print FILENAME "\t" $0}' *_bacant_out/integron.filter.tsv | fgrep -v 'QUERY' | sed 's/_bacant_out\/integron.filter.tsv//g' | sed 's/[0-9]*\~integron_..//g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >CPE_Trans_BacAnt_Integrons.tab

#---------- Identify IS using Prokka (NOTE: I used an updated ISdb (default ISdb for prokka) located in my desktop)
time awk '{print FILENAME "\t" $0}' *_prokka_out/*.gff | grep 'ISfinder'  | sed 's/ /_/g' | awk '{print $1 "\t" $2 "\t" $NF}' | sed -e 's/ID=.*ISfinder://g' -e 's/;locus_tag.*//g' -e 's/html.2020\/\///g' | sed 's/_prokka_out.*gff/_assembly_renamed.fasta/g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >../../VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis/6_Prokka_InsertionSequences/CPE_Trans_Prokka_IS.tab 



#---------- Identity AMR genes using NCBI AMRfinderplus

# NOTE: Ran this earlier, so just copying the results

mkdir ../3_amrfinderplus_results 

#---------- Identity Virulence factor genes using Abricate

for d in $(ls *.fasta); do echo "abricate -db vfdb $d >"$d".vfdb.tab"; done >abricate_cmds.sh
time parallel --jobs 62 < abricate_cmds.sh &

mkdir ../4_abricate_vf_results 
mv *.vfdb.tab ../4_abricate_vf_results
cd ../4_abricate_vf_results

cat *_assembly_renamed.fasta.vfdb.tab | fgrep -v '#' | awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6}' >CPE_Trans_Abricate_VF_Genes.tab 


## Excluding possible contamination samples
-rwxrwxr-x 1 prakki prakki 9.8M Jul  6 14:17 batch12_27072019_ENT1410_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:17 batch2_21032019_ENT466_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:17 batch2_21032019_ENT497_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:18 batch2_21032019_ENT378_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:18 rbatch2_07082020_ENT792_assembly_renamed.fasta

# These assemblies have abnormal assembly sizes. So excluding them from analysis

## -- Reunning MLST 

for d in $(ls *.fasta); do echo "mlst $d >"$d".mlst.tab"; done >mlst_cmds.sh
time parallel --jobs 62 < mlst_cmds.sh &

Ran the below commands in R

library(dplyr)
library(UpSetR)
library(tidyverse)
library(splitstackshape)
library(reshape2)
library(randomcoloR)
library(ComplexHeatmap)
library(data.table)
library(kableExtra)
library(DT)
library(data.table)
library(formattable)




setwd("/data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis")


hybridAssemb_1198list <- readLines("/data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis/CPE_Hyrbid_Assemblies_1198.list")
#head(hybridAssemb_1198list)

###@@@@@@@@@@@@@@ Dataframe1 - PlasClass Predictions

plasClass_Prob_df <- data.table::fread(file = "1_plasClass_probs/CPE_Trans_PlasClass_Predictions_comb.tab", sep = "\t", header = FALSE)
#head(plasClass_Prob_df)

colnames(plasClass_Prob_df) <- c("Fasta", "Contig", "Probability")

plasClass_Prob_df <- 
  plasClass_Prob_df %>%  
  mutate(Fasta = stringr::str_replace(Fasta, ".plasclass.probs.out", "")) %>% 
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#')) %>% 
  mutate(Classification = if_else(Probability >= 0.6, "Plasmid", "Chromosome")) 

#head(plasClass_Prob_df)

###@@@@@@@@@@@@@@ Dataframe2 - MLST

mlst_df <- data.table::fread(file = "5_MLST/mlst_log2.19", sep = "\t", header = FALSE, fill = TRUE)
#head(mlst_df) # mlst_df now has 1289 assembly information
colnames(mlst_df) <- c("Fasta", "Species", "ST", "Gene1", "Gene2", "Gene3", "Gene4", "Gene5", "Gene6", "Gene7")
mlst_df <- mlst_df %>% filter(Fasta %in% hybridAssemb_1198list) # mlst_df now has only 1198 assembly information

Species_Counts <- as.data.frame(table(mlst_df$Species))
colnames(Species_Counts) <- c("Species","SampleCount")

Species_Counts %>% 
  kbl(caption = "Species and Sample Counts") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Species and Sample Counts

Species	SampleCount
	16
cfreundii	75
cronobacter	6
ecloacae	176
ecoli	375
kaerogenes	15
koxytoca	14
kpneumoniae	519
senterica	2

#head(Species_Counts)

#----------------Step1: Combining MLST and plasClass dataframes

plasClass_Prob_df <- left_join(plasClass_Prob_df, mlst_df, by = c("Fasta")) %>% 
    select(Fasta,Contig,Fasta_Contig,Classification,Species, ST) %>% 
    mutate(Species_ContigClass=paste(Species,Classification, sep = '#'))

#head(plasClass_Prob_df)

###@@@@@@@@@@@@@@ Dataframe2 - Bacant Predictions Transposons

bacAnt_df <- data.table::fread(file = "2_bacant_annot/CPE_Trans_BacAnt_Transposons.tab", sep = "\t", header = TRUE)
#head(bacAnt_df)

colnames(bacAnt_df) <- c("Fasta", "Contig", "Fasta_Contig", "Transposon")

#----------------Step2: Combining plasClass dataframe with bacant dataframe for plotting Transposons

bacant_plasClass_df <- left_join(bacAnt_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,Transposon,Classification,Species_ContigClass)

#head(bacant_plasClass_df)

bacant_plasClass_df <- bacant_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

# bacant_plasClass_df %>%
#   select(Transposon,Species_ContigClass) %>%
#   table() %>% # frequency of unique transposons
#   kbl(caption = "Transposons and their counts in Chromosomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

customGreen0 = "#DeF7E9"
customRed = "#ff7f7f"

bacant_plasClass_df_total_col = bacant_plasClass_df %>%
  select(Fasta.x,Transposon,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  drop_na(Species_ContigClass) %>% count() %>% c()

Transposons

Table1

Percentages of Samples carrying transposons in Chromsomes and Plasmids across Species

# percentages of transposons (https://clarewest.github.io/blog/post/making-tables-shiny/)
bacant_plasClass_df %>%
  select(Fasta.x,Transposon,Species_ContigClass) %>%
  unique() %>%
  select(Transposon,Species_ContigClass) %>%
  count(Transposon, Species_ContigClass) %>%
  rename(Freq = n) %>%
  drop_na(Species_ContigClass) %>%
  mutate(Species_ContigClass2=Species_ContigClass) %>%
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>%
  left_join(Species_Counts, by = c("Species")) %>%
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>%
  select(Transposon,Species_ContigClass,SamplePrcnt_wGeneElement) %>%
  data.table::dcast(Transposon~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>%
  replace(is.na(.), 0) %>%
  mutate_if(is.numeric, round, digits=1) %>% 
  formattable(., align = c("l",rep("c", bacant_plasClass_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:bacant_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

#head(bacant_plasClass_df)

Table2

Transposons and their unique counts in Chromsomes and Plasmids across Species

bacant_plasClass_df %>% 
  select(Transposon,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  as.data.frame.matrix() %>% 
  kbl(caption = "Transposons and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria") 

Transposons and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	kaerogenes#Plasmid	koxytoca#Chromosome	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid	senterica#Plasmid
Tn10|AP005147	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
Tn1000|KX709966	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Tn1721|X61367	0	1	1	1	0	0	0	1	1	0	0	0	0	0	1	0
Tn2|KX242350	0	1	1	0	0	0	1	1	1	0	0	0	0	0	1	0
Tn2003|AY532647	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0
Tn2006|EF127491	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn2007|EF059914	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
Tn2008|GQ861438	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn2012|EU523120	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	0
Tn21	1	0	0	1	0	1	1	1	1	0	0	0	0	1	1	0
Tn21-like	1	1	1	1	0	1	1	1	1	0	0	0	0	1	1	0
Tn21|CP001182	1	1	1	1	0	0	1	1	1	0	1	0	0	1	1	0
Tn2555.3|AY485150.1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0
Tn3411|M19532	1	0	1	0	0	0	0	1	1	0	0	0	0	1	0	0
Tn3926|X78059	0	0	1	1	0	1	0	0	1	0	0	0	0	0	1	0
Tn4|HM749966	1	0	1	1	0	0	1	1	1	0	0	0	0	1	1	0
Tn402|KC999035	1	0	0	1	0	1	1	1	1	0	0	0	0	0	1	0
Tn4352|HQ840942	1	0	1	1	0	1	0	1	1	0	0	0	0	1	1	0
Tn4401a|KT378596	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0
Tn4662a	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0
Tn501|Z00027	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn5036-like|GQ160960	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	0
Tn5036|Y09025	0	0	1	1	0	0	1	0	1	0	0	0	0	1	1	0
Tn5044/Tn5046	0	0	0	1	0	0	1	0	0	0	0	0	0	1	1	0
Tn5057|AJ302768	1	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0
Tn5060|AJ551280	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn5073|AF461013	0	0	1	1	0	1	1	1	1	0	1	0	1	1	1	0
Tn5074|AF461012	1	1	0	0	0	1	1	1	0	0	0	0	0	0	1	0
Tn5075|AF457211	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn511-3|EU287476	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Tn512|EU306744	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
Tn5393|M96392	0	0	0	0	0	1	1	1	0	0	0	0	0	0	1	0
Tn5393c|AF313472	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
Tn5403|EU287476	0	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1
Tn5718delta|AM261760	1	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0
Tn6018-L|CP001182	0	0	1	1	0	1	0	1	0	0	0	0	0	0	1	0
Tn602|AH000951	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0
Tn6022|CP001182	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn6029|HQ840942	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
Tn6082|AP005147	0	1	0	0	1	1	1	1	1	0	0	0	0	1	1	0
Tn6196|KC999035	0	1	1	1	0	1	1	1	1	0	1	1	0	1	1	0
Tn6205|CP003505	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	0
Tn6229|KF295829	0	0	0	1	0	1	1	0	0	0	0	0	0	1	1	0
Tn6238|KJ511462	0	0	0	0	0	0	1	1	1	0	0	0	0	1	1	0
Tn6292	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	0
Tn6934|CP046417.1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Tn7-like|AM261760	1	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
Tn7|AP002527	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0
Tn7051|MN915011.1	0	0	0	1	0	0	1	1	1	0	0	0	0	0	1	0
Tn801|AF080442	1	1	1	1	0	0	1	1	1	0	1	0	0	1	1	0
Tn9-like|HQ840942	1	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
Tn917|FJ208941	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0

Transposons UpsetR

UpsetR Plot Transposons

#----------------Step3: UpsetR Plot Transposons
 
tn_upsetR_df <- bacant_plasClass_df %>% select(Transposon,Species_ContigClass)
#View(tn_upsetR_df)
tn_upsetR_lt <- split(tn_upsetR_df$Transposon, tn_upsetR_df$Species_ContigClass) 
tn_comb_mat = make_comb_mat(tn_upsetR_lt)

#tn_comb_mat
#str(tn_comb_mat)

#as.data.frame(tn_comb_mat)
# comb_mat
# UpSet(tn_comb_mat)
# UpSet(t(comb_mat))
# UpSet(comb_mat, top_annotation = upset_top_annotation(comb_mat, add_numbers = TRUE),
# right_annotation = upset_right_annotation(comb_mat, add_numbers = TRUE))

 
col_size = comb_size(tn_comb_mat)
row_size = set_size(tn_comb_mat)

ht = UpSet(tn_comb_mat, 
      row_title = "Species#SeqClassification", column_title = "Transposon Intersection across species",
      pt_size = unit(6, "pt"),
      lwd = unit(2, "pt"),
      #comb_col = "red",
      # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" , "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
      #bg_col = upset_row_col$Color,
      top_annotation = upset_top_annotation(tn_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                            bar_width = 0.5, annotation_name_rot = 90),
      right_annotation = upset_right_annotation(tn_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
      show_row_names = TRUE,
      row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
      width = unit(700, units = "pt"), height = unit(10, "cm"))

ht = draw(ht)
#ht
col_od = column_order(ht)
row_od = row_order(ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

Transposons proportions in Chromsome and plasmids by Species

bacant_TransposonCounts <- bacant_plasClass_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,Transposon,ContigClass) %>% 
  unique() %>% 
  mutate(Species_Transposon=paste(Species, Transposon, sep = '#')) %>% 
  select(Species_Transposon,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#Transposon") %>% 
  separate("Species#Transposon", sep = "#", into = c("Species", "Transposon")) %>% 
  group_by(Species) %>% 
  summarise(Count_Transposon_in_Chr_old=sum(Chromosome), Count_Transposon_in_Plasmid_old=sum(Plasmid), Count_Transposon_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_Transposon_in_Chr = Count_Transposon_in_Chr_old - Count_Transposon_in_Chr_Plasmid, 
         Count_Transposon_in_Plasmid = Count_Transposon_in_Plasmid_old - Count_Transposon_in_Chr_Plasmid) %>% 
  select(Species,Count_Transposon_in_Chr,Count_Transposon_in_Plasmid,Count_Transposon_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
bacant_TransposonCounts_props <- bacant_TransposonCounts*100/rowSums(bacant_TransposonCounts) 

bacant_TransposonCounts_props %>% 
  kbl(caption = "Transposons proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Transposons proportions in Chromsome and plasmids by Species

	Count_Transposon_in_Chr	Count_Transposon_in_Plasmid	Count_Transposon_in_Chr_Plasmid
	39.393939	24.24242	36.36364
cfreundii	15.151515	36.36364	48.48485
cronobacter	0.000000	100.00000	0.00000
ecloacae	12.903226	32.25806	54.83871
ecoli	13.157895	15.78947	71.05263
kaerogenes	0.000000	80.00000	20.00000
koxytoca	12.500000	75.00000	12.50000
kpneumoniae	7.692308	30.76923	61.53846
senterica	0.000000	100.00000	0.00000

Plot2

Transposons Percentages in Chromsome and plasmids by Species

bacant_TransposonCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "Transposon location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `Transposon location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Transposons by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

Integrons

Table1

Percentages of Samples carrying Integrons in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe2 - Bacant Predictions Integrons

bacAnt_Integrons_df <- data.table::fread(file = "2_bacant_annot/CPE_Trans_BacAnt_Integrons.tab", sep = "\t", header = TRUE)
#head(bacAnt_Integrons_df)

colnames(bacAnt_Integrons_df) <- c("Fasta", "Contig", "Fasta_Contig", "Integron")

#----------------Step2: Combining plasClass dataframe with bacant dataframe for plotting Transposons

bacAnt_Integrons_plasClass_df <- left_join(bacAnt_Integrons_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,Integron,Classification,Species_ContigClass)

#head(bacAnt_Integrons_plasClass_df)

bacAnt_Integrons_plasClass_df <- bacAnt_Integrons_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # bacAnt_Integrons_plasClass_df now has only 1198 assembly information

# bacAnt_Integrons_plasClass_df %>%
#   select(Integron,Species_ContigClass) %>%
#   table() %>% # frequency of unique Integrons
#   kbl(caption = "Integrons and their counts in Chromsomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

bacAnt_Integrons_plasClass_df_total_col = bacAnt_Integrons_plasClass_df %>%
  select(Fasta.x,Integron,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()

# percentages of Integrons
bacAnt_Integrons_plasClass_df %>% 
  select(Fasta.x,Integron,Species_ContigClass) %>%
  unique() %>% 
  select(Integron,Species_ContigClass) %>% 
  count(Integron, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(Integron,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(Integron~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
  #kbl(caption = "Sample count percentages for the Integrons in Chromsomes and Plasmids across Species") %>% 
  #kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", bacAnt_Integrons_plasClass_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:bacAnt_Integrons_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

bacAnt_Integrons_plasClass_df %>% 
  select(Integron,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique Integrons
  kbl(caption = "Integrons and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Integrons and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Plasmid	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid
In1000|KF921521	0	0	0	0	0	0	0	0	0	0	0	0	1
In1002|KF921555	0	0	0	0	0	0	0	0	1	0	0	0	0
In1003|KF921556	0	0	0	0	0	0	0	1	1	0	0	0	1
In1004|KF921558	0	1	0	0	0	0	0	1	1	0	0	0	1
In1015|KJ420612	0	0	0	0	0	0	1	0	0	0	0	0	1
In104|AY463797	0	0	0	0	0	0	0	0	1	0	0	0	0
In1069|KM589497	0	0	0	1	0	0	1	1	0	0	0	1	1
In1114|KF914309	0	0	0	0	0	0	0	1	1	0	0	0	0
In1133|KM595287	0	0	0	0	0	1	0	0	0	0	0	0	0
In1207|KP901091	0	0	0	0	0	0	0	0	1	0	0	0	0
In1227|KR699626	0	0	0	0	0	0	0	0	0	0	0	0	1
In1239|KT290951	0	1	0	0	0	1	0	0	1	0	0	0	1
In1249|KT316808	0	0	1	0	0	0	0	1	1	0	0	0	1
In1330|MF612148	0	0	0	1	0	0	0	1	1	0	0	1	1
In1368|KY047413	0	0	1	0	0	0	0	0	0	0	0	1	0
In1372|LC224310	0	0	0	0	0	0	0	0	0	0	0	0	1
In141|FJ711659	0	0	0	1	0	0	0	0	1	0	0	0	1
In1411|KY426918	0	0	0	0	0	0	0	1	1	0	0	0	0
In1440|LC318534	1	0	0	0	0	0	0	1	1	0	0	0	0
In1469|LC333379	0	0	0	1	0	0	1	0	0	0	0	1	1
In149|FJ457611	0	0	0	0	0	1	0	0	0	0	0	0	1
In152|HQ386839	0	0	0	0	0	0	0	0	0	0	0	0	1
In153|JF701188	0	0	0	0	0	0	0	0	0	0	0	0	1
In193|DQ647028	0	0	0	0	0	0	1	0	0	0	0	0	1
In2-10|KF188201	1	0	1	0	0	0	0	1	0	0	0	0	0
In2-3|JX867127	0	0	0	0	0	0	0	0	0	0	0	1	0
In2-30|AB234887	0	0	0	0	0	0	0	0	0	0	0	1	0
In2-44|FJ591055	0	0	1	0	0	0	0	1	0	0	0	0	0
In206|FM957884	0	0	0	0	0	0	1	0	0	0	0	0	1
In221|DQ372710	0	0	0	0	0	0	0	0	1	0	0	0	0
In240|EF368053	0	1	0	1	0	0	1	1	0	1	0	0	1
In244|KC543497	0	0	0	0	0	1	0	0	0	0	0	0	0
In261|FN824509	0	0	0	0	0	0	1	1	0	0	0	0	0
In262|FN823039	0	0	0	0	0	0	0	0	0	0	0	0	1
In282|AY522431	0	0	0	0	0	0	0	1	1	0	0	1	0
In29|AF329699	0	0	0	0	0	0	0	0	0	0	0	0	1
In366|AY069972	0	0	0	0	0	0	1	0	0	0	0	0	0
In384|FM877477	0	0	0	0	0	0	0	0	1	0	0	0	0
In404|DQ520934	0	0	0	0	0	0	0	0	1	0	0	0	1
In415|EU934512	0	0	0	0	0	0	1	0	0	0	0	0	0
In440|AF227505	1	0	0	0	0	0	0	0	1	0	0	0	1
In473|FJ854362	1	0	0	0	0	0	0	1	0	0	0	0	0
In49|EU434618	0	0	0	1	0	0	1	0	0	0	0	1	1
In498|AY214164	1	1	1	1	1	1	1	1	1	1	1	1	1
In504|GU060321	0	0	0	0	0	1	0	1	1	0	0	0	1
In510|EU853659	0	0	0	1	0	1	1	1	1	1	0	1	1
In516|DQ323053	0	0	0	0	0	1	0	0	0	0	0	0	0
In538|KC200566	0	0	0	1	0	0	1	0	1	0	0	0	1
In54|AF220757	0	0	0	0	0	0	0	1	1	0	0	0	0
In545|EU259884	0	0	0	0	0	0	0	0	0	0	0	0	1
In546|FJ594766	0	0	0	0	0	0	0	0	0	0	0	0	1
In554|AJ971343	0	0	0	1	0	0	0	0	0	0	0	0	0
In555|EU675686	0	0	0	1	0	0	1	0	1	0	0	0	1
In556|HQ170516	0	0	0	1	0	1	1	0	1	0	0	0	1
In570|AF453998	0	0	1	0	0	1	1	1	1	0	0	0	0
In573|AM237806	0	0	0	0	0	0	0	1	0	0	0	0	0
In578|FN396877	0	0	0	0	0	0	0	0	0	0	0	0	1
In62|FJ594767	0	0	0	0	0	0	0	0	0	0	0	0	1
In653|HQ730120	0	0	0	0	0	0	0	0	1	0	0	0	0
In663|HQ875011	0	0	0	0	0	0	0	0	1	0	0	0	1
In710|EU434603	0	0	0	0	0	0	0	0	0	0	0	0	1
In718|JF729199	0	0	0	0	0	0	0	1	1	0	0	0	1
In73|AF322577	0	0	0	0	0	0	0	0	1	0	0	0	0
In761|AF175203	0	0	0	0	0	0	0	1	0	0	0	0	1
In771|JX131371	0	0	0	0	0	0	1	0	0	0	0	0	0
In784|JQ837988	0	0	0	0	0	0	0	0	1	0	0	0	0
In79|HQ730118	0	0	0	1	0	0	0	0	0	0	0	0	1
In792|JQ823014	0	0	0	0	0	0	0	0	0	0	0	0	1
In800|JX041889	0	0	0	0	0	0	1	0	0	0	0	0	0
In879|JX885645	0	0	1	0	0	0	0	1	1	0	0	0	1
In901|AY127872	0	0	0	0	0	0	0	0	1	0	0	0	0
In970|KF525327	0	0	0	1	0	0	0	0	0	0	0	0	0

Integrons UpsetR

#----------------Step3: UpsetR Plot Integrons

integron_upsetR_df <- bacAnt_Integrons_plasClass_df %>% select(Integron,Species_ContigClass)
#View(integron_upsetR_df)
integron_upsetR_lt <- split(integron_upsetR_df$Integron, integron_upsetR_df$Species_ContigClass) 
integron_comb_mat = make_comb_mat(integron_upsetR_lt)

#integron_comb_mat
#str(integron_comb_mat)

#as.data.frame(integron_comb_mat)
# comb_mat
#UpSet(integron_comb_mat)


int_col_size = comb_size(integron_comb_mat)
int_row_size = set_size(integron_comb_mat)

integron_ht = UpSet(integron_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Integron Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
#           bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" , "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(integron_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(integron_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(700, units = "pt"), height = unit(10, "cm"))

ht = draw(integron_ht)
#ht
int_col_od = column_order(integron_ht)
int_row_od = row_order(integron_ht)

decorate_annotation("intersection_size", {
  grid.text(int_col_size[int_col_od], 
            seq_len(length(int_col_size)), 
            unit(int_col_size[int_col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

Integrons proportions in Chromsome and plasmids by Species

bacant_IntegronCounts <- bacAnt_Integrons_plasClass_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,Integron,ContigClass) %>% 
  unique() %>% 
  mutate(Species_Integron=paste(Species, Integron, sep = '#')) %>% 
  select(Species_Integron,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#Integron") %>% 
  separate("Species#Integron", sep = "#", into = c("Species", "Integron")) %>% 
  group_by(Species) %>% 
  summarise(Count_Integron_in_Chr_old=sum(Chromosome), Count_Integron_in_Plasmid_old=sum(Plasmid), Count_Integron_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_Integron_in_Chr = Count_Integron_in_Chr_old - Count_Integron_in_Chr_Plasmid, 
         Count_Integron_in_Plasmid = Count_Integron_in_Plasmid_old - Count_Integron_in_Chr_Plasmid) %>% 
  select(Species,Count_Integron_in_Chr,Count_Integron_in_Plasmid,Count_Integron_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
bacant_IntegronCounts_props <- bacant_IntegronCounts*100/rowSums(bacant_IntegronCounts) 

bacant_IntegronCounts_props %>% 
  kbl(caption = "Integrons proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Integrons proportions in Chromsome and plasmids by Species

	Count_Integron_in_Chr	Count_Integron_in_Plasmid	Count_Integron_in_Chr_Plasmid
	50.000000	37.50000	12.50000
cfreundii	30.000000	65.00000	5.00000
cronobacter	0.000000	100.00000	0.00000
ecloacae	25.000000	58.33333	16.66667
ecoli	20.000000	42.50000	37.50000
kaerogenes	0.000000	100.00000	0.00000
koxytoca	0.000000	100.00000	0.00000
kpneumoniae	9.090909	77.27273	13.63636

Plot2

Integrons Percentages in Chromsome and plasmids by Species

bacant_IntegronCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "Integron location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `Integron location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Integrons by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

AMR Genes

Table1

Percentages of Samples carrying AMR genes in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe3 - AMRfinderplus - AMR Genes

amrFinderplus_df <- data.table::fread(file = "3_amrfinderplus_results/CPE_Trans_AMRFinderplus_AMR_Genes.tab", sep = "\t", header = TRUE)
#head(amrFinderplus_df)

colnames(amrFinderplus_df) <- c("Fasta", "Contig", "AMR_GENE", "AMR_GENE_CLASS","Coverage","Identity")

amrFinderplus_df <- amrFinderplus_df %>% filter(Fasta %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

amrFinderplus_df <- 
  amrFinderplus_df %>%  
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#'))

#----------------Step2: Combining plasClass dataframe with AMRFinderplus dataframe for plotting AMR genes

amrFinderplus_df <- left_join(amrFinderplus_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,AMR_GENE,AMR_GENE_CLASS,Classification,Species_ContigClass)

amrFinderplus_df_total_col = amrFinderplus_df %>%
  select(Fasta.x,AMR_GENE,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()

# percentages of AMR_GENEs
amrFinderplus_df %>% 
  select(Fasta.x,AMR_GENE,Species_ContigClass) %>%
  unique() %>% 
  select(AMR_GENE,Species_ContigClass) %>% 
  count(AMR_GENE, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(AMR_GENE,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(AMR_GENE~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
  #kbl(caption = "Sample count percentages for the AMR genes in Chromsomes and Plasmids across Species") %>% 
  #kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", amrFinderplus_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:amrFinderplus_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5,10,25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

### AMR Genes and their unique counts in Chromsomes and Plasmids across Species

amrFinderplus_df %>% 
  select(AMR_GENE,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "AMR Genes and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

AMR Genes and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Chromosome	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	kaerogenes#Plasmid	koxytoca#Chromosome	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid	senterica#Plasmid
aac(3)-IId	1	1	1	1	0	1	1	1	1	1	0	0	0	1	0	1	0
aac(3)-IIe	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0
aac(3)-IIg	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
aac(3)-IVa	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	1	0
aac(6’)-Ib	1	1	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0
aac(6’)-Ib-cr5	0	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
aac(6’)-Ib’	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
aac(6’)-Ib3	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0
aac(6’)-Ib4	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0
aac(6’)-If	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
aac(6’)-IIc	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
aac(6’)-Il	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
aacA34	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
aadA1	1	1	1	1	0	0	1	1	1	1	0	1	0	0	1	1	0
aadA16	0	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	0
aadA2	1	1	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
aadA22	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
aadA5	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
aadA8	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ampC	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ant(2’’)-Ia	0	1	1	1	0	0	0	1	1	0	0	0	0	0	0	1	0
aph(3’’)-Ib	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0
aph(3’)-Ia	1	0	0	1	0	0	1	0	1	1	0	0	0	0	1	1	0
aph(3’)-IIa	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
aph(3’)-VI	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0
aph(3’)-XV	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
aph(4)-Ia	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	1	0
aph(6)-Id	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0
armA	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
arr	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
arr-2	0	0	0	1	0	0	0	1	0	1	0	1	0	0	1	1	0
arr-3	0	1	0	1	0	1	0	1	0	1	0	1	0	0	1	1	0
blaACT-1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-16	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-17	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-24	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-25	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-28	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-40	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-45	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
blaACT-49	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-51	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-52	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-53	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-64	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-65	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-69	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-74	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaACT-90	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaCARB-2	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
blaCMH-4	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaCMY-109	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-148	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaCMY-152	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-159	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
blaCMY-35	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-39	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-4	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	1	0
blaCMY-42	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaCMY-48	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-6	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
blaCMY-65	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-66	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-75	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-84	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCTX-M-14	1	0	1	0	0	0	1	0	1	1	0	0	0	0	0	1	0
blaCTX-M-15	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
blaCTX-M-27	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-3	1	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0
blaCTX-M-55	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-65	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-8	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaCTX-M-9	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
blaDHA-1	1	0	0	1	0	0	1	0	0	1	0	1	0	0	1	1	0
blaDHA-15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaDHA-17	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaDHA-21	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaDHA-4	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaEC-5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
blaGES-9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaIMI-1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaIMP-1	0	1	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0
blaIMP-26	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaIMP-4	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
blaKPC-2	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
blaKPC-6	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
blaLAP-1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaLAP-2	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	1	0
blaLEN-17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaMAL-1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaMAL-2	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaMIR-20	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaMIR-5	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
blaNDM-1	0	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	1
blaNDM-4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaNDM-5	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	1	0
blaNDM-7	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0
blaNDM-9	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaOKP-A-11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-A-17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-A-9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-34	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOKP-B-8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaOXA-1	1	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0
blaOXA-10	0	0	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0
blaOXA-181	0	0	1	0	0	0	0	1	0	1	0	0	0	0	1	1	0
blaOXA-2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
blaOXA-21	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaOXA-23	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaOXA-232	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
blaOXA-48	0	0	0	1	0	1	0	1	0	1	0	0	0	0	0	1	0
blaOXA-9	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0
blaOXY-1-1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
blaOXY-1-2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
blaOXY-2-1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
blaPER-7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaSFO-1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0
blaSHV-1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
blaSHV-108	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-11	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0
blaSHV-12	1	0	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
blaSHV-144	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-186	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-187	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-26	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-27	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-28	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-2A	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaSHV-32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-33	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-5	0	0	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0
blaSHV-52	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-60	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-62	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-75	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-76	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaSHV-77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
blaTEM-1	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
blaTEM-116	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
blaTEM-135	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaTEM-169	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaTEM-176	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
blaTEM-190	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
blaTEM-2	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaTEM-215	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
blaTEM-30	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaTEM-31	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaTEM-32	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
blaVIM-4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
ble	0	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	1
catA1	1	0	1	0	0	0	1	1	1	1	0	1	0	0	0	1	0
catA2	0	0	1	1	0	0	1	1	0	1	0	0	0	0	0	1	0
catB	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0
catB3	0	1	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0
catB8	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
cmlA1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
cmlA5	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	1	0
dfrA1	1	0	1	1	0	0	1	1	1	0	0	0	0	0	1	1	0
dfrA12	1	0	0	1	0	0	1	1	1	1	0	1	0	0	1	1	0
dfrA14	0	1	0	1	0	0	1	1	1	1	0	1	0	1	1	1	0
dfrA15	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0
dfrA16	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0
dfrA17	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0
dfrA23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
dfrA27	0	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	0
dfrA29	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
dfrA32	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0
dfrA33	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0
dfrA5	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
dfrA7	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
dfrB4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ere(A)	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0
erm(42)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
floR	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0
fosA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
fosA10	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0
fosA3	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
fosA4	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
fosA7.5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
lnu(F)	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
mcr-1.1	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0
mcr-10.1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0
mcr-3.1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
mcr-4.3	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
mef(B)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
mph(A)	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0
mph(E)	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
msr(E)	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0
oqxA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxA10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxA11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB14	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB19	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
oqxB6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
qacEdelta1	1	1	1	1	0	1	1	1	1	1	0	1	0	0	1	1	0
qacG2	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0
qacL	0	1	1	1	0	0	0	1	1	1	0	0	0	0	0	1	0
qepA1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
qepA4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
qepA8	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
qnrA1	0	0	0	1	0	0	0	1	1	1	0	0	0	0	0	1	0
qnrB1	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0
qnrB12	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB17	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB2	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0
qnrB28	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB35	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB38	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB4	0	0	0	1	0	0	1	0	0	1	0	1	0	0	1	1	0
qnrB6	0	1	1	1	0	0	0	0	0	1	0	1	0	0	0	1	0
qnrB69	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB7	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0
qnrB9	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	0
qnrS1	0	1	1	1	0	0	0	1	1	1	0	1	0	0	0	1	0
qnrS2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
rmtB1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
rmtC	0	1	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0
rmtF1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0
rmtF2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
sat2	1	0	1	1	0	0	1	0	1	0	0	0	0	0	1	0	0
smr	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
sul1	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0
sul2	1	1	0	1	0	0	1	1	1	1	0	1	0	1	1	1	0
sul3	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
tet(A)	1	1	0	1	0	0	1	1	1	1	0	1	0	1	1	1	0
tet(B)	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0
tet(C)	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	1	0
tet(D)	0	0	0	1	0	0	0	1	0	1	0	1	0	0	1	1	0
tet(G)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
tet(M)	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
tet(X4)	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0

AMR Genes UpsetR

#----------------Step3: UpsetR Plot AMR Genes
amr_upsetR_df <- amrFinderplus_df %>% select(AMR_GENE,Species_ContigClass)

amr_upsetR_lt <- split(amr_upsetR_df$AMR_GENE, amr_upsetR_df$Species_ContigClass) 
amr_comb_mat = make_comb_mat(amr_upsetR_lt)

col_size = comb_size(amr_comb_mat)
row_size = set_size(amr_comb_mat)

amr_ht = UpSet(amr_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "AMR Genes Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(amr_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(amr_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

amr_ht = draw(amr_ht)
#ht
col_od = column_order(amr_ht)
row_od = row_order(amr_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table3

AMR Genes counts in Chromsome and plasmids by Species

amrFinderplus_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>% 
  select(Species,AMR_GENE,ContigClass) %>% 
  unique() %>% 
  mutate(Species_AMR_GENE=paste(Species, AMR_GENE, sep = '#')) %>% 
  select(Species_AMR_GENE,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#AMRGEnes") %>% 
  separate("Species#AMRGEnes", sep = "#", into = c("Species", "AMRGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_AMRgenes_in_Chr_old=sum(Chromosome), Count_AMRgenes_in_Plasmid_old=sum(Plasmid), Count_AMRgenes_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_AMRgenes_in_Chr = Count_AMRgenes_in_Chr_old - Count_AMRgenes_in_Chr_Plasmid, 
         Count_AMRgenes_in_Plasmid = Count_AMRgenes_in_Plasmid_old - Count_AMRgenes_in_Chr_Plasmid) %>% 
  select(Species,Count_AMRgenes_in_Chr,Count_AMRgenes_in_Plasmid,Count_AMRgenes_in_Chr_Plasmid) %>% 
  mutate(Total_AMRGenes = rowSums(.[2:4])) %>% 
  kbl(caption = "AMR Genes counts in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

AMR Genes counts in Chromsome and plasmids by Species

Species	Count_AMRgenes_in_Chr	Count_AMRgenes_in_Plasmid	Count_AMRgenes_in_Chr_Plasmid	Total_AMRGenes
	17	27	16	60
cfreundii	24	46	17	87
cronobacter	2	20	0	22
ecloacae	30	44	30	104
ecoli	11	57	53	121
kaerogenes	1	39	0	40
koxytoca	3	20	0	23
kpneumoniae	46	74	41	161
senterica	0	2	0	2

Table4

AMR Genes proportions in Chromsome and plasmids by Species

amr_geneCounts <- amrFinderplus_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>% 
  select(Species,AMR_GENE,ContigClass) %>% 
  unique() %>% 
  mutate(Species_AMR_GENE=paste(Species, AMR_GENE, sep = '#')) %>% 
  select(Species_AMR_GENE,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#AMRGEnes") %>% 
  separate("Species#AMRGEnes", sep = "#", into = c("Species", "AMRGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_AMRgenes_in_Chr_old=sum(Chromosome), Count_AMRgenes_in_Plasmid_old=sum(Plasmid), Count_AMRgenes_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_AMRgenes_in_Chr = Count_AMRgenes_in_Chr_old - Count_AMRgenes_in_Chr_Plasmid, 
         Count_AMRgenes_in_Plasmid = Count_AMRgenes_in_Plasmid_old - Count_AMRgenes_in_Chr_Plasmid) %>% 
  select(Species,Count_AMRgenes_in_Chr,Count_AMRgenes_in_Plasmid,Count_AMRgenes_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
amr_geneCounts_props <- amr_geneCounts*100/rowSums(amr_geneCounts) 

amr_geneCounts_props %>% 
  kbl(caption = "AMR Genes proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

AMR Genes proportions in Chromsome and plasmids by Species

	Count_AMRgenes_in_Chr	Count_AMRgenes_in_Plasmid	Count_AMRgenes_in_Chr_Plasmid
	28.333333	45.00000	26.66667
cfreundii	27.586207	52.87356	19.54023
cronobacter	9.090909	90.90909	0.00000
ecloacae	28.846154	42.30769	28.84615
ecoli	9.090909	47.10744	43.80165
kaerogenes	2.500000	97.50000	0.00000
koxytoca	13.043478	86.95652	0.00000
kpneumoniae	28.571429	45.96273	25.46584
senterica	0.000000	100.00000	0.00000

Plot2

AMR Genes Percentages in Chromsome and plasmids by Species

amr_geneCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "AMR gene location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `AMR gene location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of AMR genes by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

VF Genes

Table1

Percentages of Samples carrying Virulence genes in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe4 - Abricate - Virulence Factor Genes

Abricate_vf_df <- data.table::fread(file = "4_abricate_vf_results/CPE_Trans_Abricate_VF_Genes.tab", sep = "\t", header = TRUE)
#head(Abricate_vf_df)

##########-------------- Modifying dataframe START -------------------################

colnames(Abricate_vf_df ) <- c("Fasta", "Contig","START","END","STRAND","VF_GENE")

Abricate_vf_df <- 
  Abricate_vf_df %>%  
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#'))

Abricate_vf_df <- Abricate_vf_df %>% filter(Fasta %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

#head(Abricate_vf_df)

#----------------Step2: Combining plasClass dataframe with abricate_vf_dataframe for plotting virulence genes

Abricate_vf_df <- left_join(Abricate_vf_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,VF_GENE,Classification,Species_ContigClass)

#View(Abricate_vf_df)

Abricate_vf_df_total_col = Abricate_vf_df %>%
  select(Fasta.x,VF_GENE,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  drop_na(Species_ContigClass) %>% count() %>% c()

# percentages of AMR_GENEs
Abricate_vf_df %>% 
  select(Fasta.x,VF_GENE,Species_ContigClass) %>%
  unique() %>% 
  select(VF_GENE,Species_ContigClass) %>% 
  count(VF_GENE, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(VF_GENE,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(VF_GENE~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
 # kbl(caption = "Sample count percentages for the Virulence factor genes in Chromsomes and Plasmids across Species") %>% 
 # kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", Abricate_vf_df_total_col $n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:Abricate_vf_df_total_col $n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

Virulence genes and their unique counts in Chromsomes and Plasmids across Species

Abricate_vf_df %>% 
  select(VF_GENE,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "Virulence factors genes and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Virulence factors genes and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Chromosome	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	koxytoca#Chromosome	kpneumoniae#Chromosome	kpneumoniae#Plasmid	senterica#Chromosome
aap/aspU	0	0	0	0	0	0	0	1	0	0	0	0	0
afaA	0	0	0	0	0	0	1	0	0	0	0	0	0
afaB-I	0	0	0	0	0	0	1	0	0	0	0	0	0
afaC-I	0	0	0	0	0	0	1	0	0	0	0	0	0
afaD	0	0	0	0	0	0	1	0	0	0	0	0	0
afaE-I	0	0	0	0	0	0	1	0	0	0	0	0	0
aslA	1	1	0	0	0	0	1	0	0	0	1	0	0
astA	0	1	0	0	0	1	1	1	0	1	1	1	0
cesAB	0	0	0	0	0	0	1	0	0	0	0	0	0
cesD	0	0	0	0	0	0	1	0	0	0	0	0	0
cesD2	0	0	0	0	0	0	1	0	0	0	0	0	0
cesL	0	0	0	0	0	0	1	0	0	0	0	0	0
cesT	0	0	0	0	0	0	1	0	0	0	0	0	0
cheY	1	1	0	0	0	0	0	0	0	0	0	0	0
chuA	0	0	0	1	0	0	1	0	0	0	0	0	1
chuS	0	1	0	0	0	0	1	0	0	0	0	0	0
chuT	0	1	0	0	0	0	1	0	0	0	0	0	0
chuU	0	1	0	0	0	0	1	0	0	0	0	0	0
chuV	0	1	0	0	0	0	1	0	0	0	0	0	0
chuW	0	1	0	0	0	0	1	0	0	0	0	0	0
chuX	0	1	0	1	0	0	1	0	0	0	0	0	1
chuY	0	1	0	0	0	0	1	0	0	0	0	0	0
cnf1	0	0	0	0	0	0	1	1	0	0	0	0	0
csgA	1	1	0	0	0	0	0	0	0	0	0	0	0
csgB	1	1	0	1	0	0	1	0	0	0	0	0	1
csgD	1	1	0	1	1	0	1	0	0	0	0	0	1
csgE	1	1	0	1	0	0	0	0	0	0	0	0	1
csgF	1	1	0	1	0	0	1	0	0	0	0	0	1
csgG	1	1	0	1	1	0	1	0	0	0	0	0	1
csnA	0	0	0	0	0	0	1	0	0	0	0	0	0
daaD	0	0	0	0	0	0	1	0	0	0	0	0	0
daaE	0	0	0	0	0	0	1	0	0	0	0	0	0
daaF	0	0	0	0	0	0	1	0	0	0	0	0	0
draA	0	0	0	0	0	0	1	0	0	0	0	0	0
draD	0	0	0	0	0	0	1	0	0	0	0	0	0
draE2	0	0	0	0	0	0	1	0	0	0	0	0	0
draP	0	0	0	0	0	0	1	0	0	0	0	0	0
eae	0	0	0	0	0	0	1	0	0	0	0	0	0
east1	0	0	0	1	0	0	0	0	0	0	0	0	0
entA	0	1	0	1	1	0	1	0	1	1	1	0	1
entB	1	1	0	1	1	0	1	0	1	1	1	0	1
entC	1	1	0	0	0	0	1	0	0	0	0	0	0
entD	0	1	0	0	0	0	1	0	0	0	0	0	0
entE	1	1	0	1	0	0	1	0	0	0	0	0	1
entF	0	1	0	0	0	0	1	0	0	0	0	0	0
entS	1	1	0	1	0	0	1	0	0	0	0	0	1
escC	0	0	0	0	0	0	1	0	0	0	0	0	0
escD	0	0	0	0	0	0	1	0	0	0	0	0	0
escE	0	0	0	0	0	0	1	0	0	0	0	0	0
escF	0	0	0	0	0	0	1	0	0	0	0	0	0
escG	0	0	0	0	0	0	1	0	0	0	0	0	0
escI	0	0	0	0	0	0	1	0	0	0	0	0	0
escJ	0	0	0	0	0	0	1	0	0	0	0	0	0
escL	0	0	0	0	0	0	1	0	0	0	0	0	0
escN	0	0	0	0	0	0	1	0	0	0	0	0	0
escO	0	0	0	0	0	0	1	0	0	0	0	0	0
escP	0	0	0	0	0	0	1	0	0	0	0	0	0
escR	0	0	0	0	0	0	1	0	0	0	0	0	0
escS	0	0	0	0	0	0	1	0	0	0	0	0	0
escT	0	0	0	0	0	0	1	0	0	0	0	0	0
escU	0	0	0	0	0	0	1	0	0	0	0	0	0
escV	0	0	0	0	0	0	1	0	0	0	0	0	0
espA	0	0	0	0	0	0	1	0	0	0	0	0	0
espB	0	0	0	0	0	0	1	0	0	0	0	0	0
espD	0	0	0	0	0	0	1	0	0	0	0	0	0
espG	0	0	0	0	0	0	1	0	0	0	0	0	0
espK	0	0	0	0	0	0	1	0	0	0	0	0	0
espL1	0	1	0	0	0	0	1	0	0	0	0	0	0
espL4	0	0	0	0	0	0	1	0	0	0	0	0	0
espR1	0	0	0	0	0	0	1	0	0	0	0	0	0
espR3	0	0	0	0	0	0	1	0	0	0	0	0	0
espR4	0	0	0	0	0	0	1	0	0	0	0	0	0
espX1	0	0	0	0	0	0	1	1	0	0	0	0	0
espX2	0	0	0	0	0	0	1	0	0	0	0	0	0
espX4	0	0	0	0	0	0	1	0	0	0	0	0	0
espX5	0	0	0	0	0	0	1	0	0	0	0	0	0
espY1	0	0	0	0	0	0	1	0	0	0	0	0	0
espY2	0	1	0	0	0	0	1	0	0	0	0	0	0
espY3	0	0	0	0	0	0	1	0	0	0	0	0	0
espY4	0	0	0	0	0	0	1	0	0	0	0	0	0
etgA	0	0	0	0	0	0	1	0	0	0	0	0	0
faeC	0	0	0	0	0	0	0	1	0	0	0	0	0
faeD	0	0	0	0	0	0	0	1	0	0	0	0	0
faeE	0	0	0	0	0	0	0	1	0	0	0	0	0
faeF	0	0	0	0	0	0	0	1	0	0	0	0	0
faeH	0	0	0	0	0	0	0	1	0	0	0	0	0
faeI	0	0	0	0	0	0	0	1	0	0	0	0	0
fdeC	0	1	0	0	0	0	1	0	0	0	0	0	0
fepA	0	1	0	0	0	0	1	0	0	0	0	0	0
fepB	0	1	0	0	0	0	1	0	0	0	0	0	0
fepC	1	1	0	1	0	0	1	0	1	0	1	0	1
fepD	1	1	0	0	1	0	1	0	0	0	1	0	0
fepG	1	1	0	1	0	0	1	0	0	0	1	0	0
fes	0	1	0	0	0	0	1	0	0	0	0	0	0
fimA	0	1	0	0	0	0	1	0	0	0	0	0	0
fimB	0	1	0	0	0	0	1	0	0	0	0	0	0
fimC	0	1	0	0	0	0	1	0	0	0	0	0	0
fimD	0	1	0	1	0	0	1	0	0	0	0	0	1
fimE	1	0	0	0	0	0	1	0	0	0	0	0	0
fimF	0	1	0	0	0	0	1	0	0	0	0	0	0
fimG	0	1	0	0	0	0	1	0	0	0	0	0	0
fimH	0	1	0	0	0	0	1	0	0	0	0	0	0
fimI	0	1	0	0	0	0	1	0	0	0	0	0	0
flgH	0	0	0	0	0	0	1	0	1	0	0	0	0
fliG	1	1	0	1	0	0	1	0	1	0	0	0	1
fliM	1	0	0	0	0	0	1	0	1	0	0	0	0
fliN	0	0	0	0	0	0	1	0	1	0	0	0	0
fliP	1	0	0	0	0	0	0	0	0	0	0	0	0
focA	0	0	0	0	0	0	1	0	0	0	0	0	0
focC	0	0	0	0	0	0	1	0	0	0	0	0	0
focD	0	0	0	0	0	0	1	0	0	0	0	0	0
focF	0	0	0	0	0	0	1	0	0	0	0	0	0
focG	0	0	0	0	0	0	1	0	0	0	0	0	0
focH	0	0	0	0	0	0	1	0	0	0	0	0	0
fyuA	0	1	0	1	1	0	1	0	1	1	1	1	1
gspC	0	1	0	0	0	0	1	0	0	0	0	0	0
gspD	0	1	0	0	0	0	1	0	0	0	0	0	0
gspE	0	1	0	0	0	0	1	0	0	0	0	0	0
gspF	0	1	0	0	0	0	1	0	0	0	0	0	0
gspG	0	1	0	0	0	0	1	0	0	0	0	0	0
gspH	0	1	0	0	0	0	1	0	0	0	0	0	0
gspI	0	1	0	0	0	0	1	0	0	0	0	0	0
gspJ	0	1	0	0	0	0	1	0	0	0	0	0	0
gspK	0	1	0	0	0	0	1	0	0	0	0	0	0
gspL	0	1	0	0	0	0	1	0	0	0	0	0	0
gspM	0	1	0	0	0	0	1	0	0	0	0	0	0
gtrA	0	0	0	0	0	0	1	1	0	0	0	0	0
gtrB	0	0	0	0	0	0	1	1	0	0	0	0	0
hlyA	0	0	0	0	0	0	1	1	0	0	0	0	0
hlyB	0	0	0	0	0	0	1	1	0	0	0	0	0
hlyC	0	0	0	0	0	0	1	1	0	0	0	0	0
hlyD	0	0	0	0	0	0	1	1	0	0	0	0	0
ibeA	0	1	0	0	0	0	1	0	0	0	0	0	0
iroB	0	0	0	0	1	0	1	1	1	0	0	1	0
iroC	0	0	0	0	1	0	1	1	0	0	0	1	0
iroD	0	0	0	0	1	0	1	1	0	0	0	1	0
iroE	0	0	0	0	1	0	1	1	0	0	0	1	0
iroN	0	0	0	0	1	0	1	1	1	0	0	1	0
irp1	0	1	0	1	1	0	1	0	1	1	1	1	1
irp2	0	1	0	1	1	0	1	0	1	1	1	1	1
iucA	0	0	1	1	0	0	1	1	0	0	0	1	1
iucB	0	0	1	1	0	0	1	1	0	0	0	1	1
iucC	0	0	1	1	0	0	1	1	0	0	0	1	1
iucD	0	0	1	0	0	0	1	1	0	0	0	1	0
iutA	0	0	1	1	0	0	1	1	0	0	0	1	1
kpsD	0	1	0	0	0	0	1	0	0	0	0	0	0
kpsM	0	1	0	0	0	0	1	0	0	0	0	0	0
kpsT	0	0	0	0	0	0	1	0	0	0	0	0	0
lpfA	0	0	0	0	1	0	0	0	0	0	0	0	0
lpfB	0	0	0	0	1	0	0	0	0	0	0	0	0
lpfC	0	0	0	0	1	0	0	0	0	0	0	0	0
lpfE	0	0	0	0	1	0	0	0	0	0	0	0	0
map	0	0	0	0	0	0	1	0	0	0	0	0	0
mgtB	0	0	0	0	0	0	0	0	0	1	0	0	0
nleB2	0	0	0	0	0	0	1	0	0	0	0	0	0
ompA	1	1	0	1	1	0	1	0	1	1	1	0	1
paa	0	0	0	0	0	0	1	1	0	0	0	0	0
papA	0	0	0	0	0	0	1	0	0	0	0	0	0
papB	0	0	0	0	0	0	1	1	0	0	0	0	0
papC	0	0	0	0	0	0	1	1	0	0	0	0	0
papD	0	0	0	0	0	0	1	1	0	0	0	0	0
papE	0	0	0	0	0	0	1	0	0	0	0	0	0
papF	0	0	0	0	0	0	1	1	0	0	0	0	0
papG	0	0	0	0	0	0	1	0	0	0	0	0	0
papH	0	0	0	0	0	0	1	1	0	0	0	0	0
papI	0	0	0	0	0	0	1	1	0	0	0	0	0
papJ	0	0	0	0	0	0	1	1	0	0	0	0	0
papK	0	0	0	0	0	0	1	1	0	0	0	0	0
papX	0	0	0	0	0	0	1	1	0	0	0	0	0
pic	0	0	0	0	0	0	1	0	0	0	0	0	0
sat	0	0	0	0	0	0	1	1	0	0	0	0	0
senB	0	0	0	1	0	0	0	1	0	0	0	0	1
sepD	0	0	0	0	0	0	1	0	0	0	0	0	0
sepL	0	0	0	0	0	0	1	0	0	0	0	0	0
sepQ/escQ	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaA	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaB	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaC	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaD	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaE	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaF	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaG	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaH	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaS	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaX	0	0	0	0	0	0	1	0	0	0	0	0	0
sfaY	0	0	0	0	0	0	1	0	0	0	0	0	0
shuA	0	1	0	0	0	0	1	0	0	0	0	0	0
shuS	0	0	0	0	0	0	1	0	0	0	0	0	0
shuT	0	0	0	0	0	0	1	0	0	0	0	0	0
shuX	0	1	0	0	0	0	1	0	0	0	0	0	0
shuY	0	0	0	0	0	0	1	0	0	0	0	0	0
sinH	0	1	0	0	0	0	0	0	0	0	0	0	0
tcpC	0	0	0	0	0	0	1	0	0	0	0	0	0
tviB	0	1	0	0	0	0	0	0	0	0	0	0	0
tviC	0	1	0	0	0	0	0	0	0	0	0	0	0
tviD	0	1	0	0	0	0	0	0	0	0	0	0	0
tviE	0	1	0	0	0	0	0	0	0	0	0	0	0
vat	0	0	0	0	0	0	1	0	0	0	0	0	0
vexA	0	1	0	0	0	0	0	0	0	0	0	0	0
vexB	0	1	0	0	0	0	0	0	0	0	0	0	0
vexC	0	1	0	0	0	0	0	0	0	0	0	0	0
vexD	0	1	0	0	0	0	0	0	0	0	0	0	0
vexE	0	1	0	0	0	0	0	0	0	0	0	0	0
yagV/ecpE	0	0	1	0	0	1	1	0	0	0	1	0	0
yagW/ecpD	0	0	1	0	0	1	1	0	1	0	1	0	0
yagX/ecpC	0	0	1	0	0	1	1	0	0	0	1	0	0
yagY/ecpB	0	0	1	0	0	0	1	0	0	0	1	0	0
yagZ/ecpA	0	0	1	0	0	1	1	0	1	1	1	0	0
ybtA	0	1	0	1	1	0	1	0	1	1	1	1	1
ybtE	0	1	0	1	1	0	1	0	1	1	1	1	1
ybtP	0	1	0	1	1	0	1	0	1	1	1	1	1
ybtQ	0	1	0	1	1	0	1	0	1	1	1	1	1
ybtS	1	1	0	1	1	0	1	0	1	1	1	1	1
ybtT	0	1	0	1	1	0	1	0	1	1	1	1	1
ybtU	0	1	0	1	1	0	1	0	1	1	1	1	1
ybtX	0	1	0	1	1	0	1	0	1	1	1	1	1
ykgK/ecpR	0	0	1	0	0	1	1	0	0	0	1	0	0

VF Genes UpsetR

#----------------Step3: UpsetR Plot VFGenes

abr_upsetR_df <- Abricate_vf_df %>% select(VF_GENE,Species_ContigClass)
#View(abr_upsetR_df)

abr_upsetR_lt <- split(abr_upsetR_df$VF_GENE, abr_upsetR_df$Species_ContigClass) 
abr_comb_mat = make_comb_mat(abr_upsetR_lt)

col_size = comb_size(abr_comb_mat)
row_size = set_size(abr_comb_mat)

vf_ht = UpSet(abr_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Virulence factor genes Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(abr_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(abr_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

vf_ht = draw(vf_ht)
#ht
col_od = column_order(vf_ht)
row_od = row_order(vf_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

VF Genes proportions in Chromsome and plasmids by Species

vf_geneCounts <- Abricate_vf_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,VF_GENE,ContigClass) %>% 
  unique() %>% 
  mutate(Species_VF_GENE=paste(Species, VF_GENE, sep = '#')) %>% 
  select(Species_VF_GENE,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#VFgenes") %>% 
  separate("Species#VFgenes", sep = "#", into = c("Species", "VFGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_VFgenes_in_Chr_old=sum(Chromosome), Count_VFgenes_in_Plasmid_old=sum(Plasmid), Count_VFgenes_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_VFgenes_in_Chr = Count_VFgenes_in_Chr_old - Count_VFgenes_in_Chr_Plasmid, 
         Count_VFgenes_in_Plasmid = Count_VFgenes_in_Plasmid_old - Count_VFgenes_in_Chr_Plasmid) %>% 
  select(Species,Count_VFgenes_in_Chr,Count_VFgenes_in_Plasmid,Count_VFgenes_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
vf_geneCounts_props <- vf_geneCounts*100/rowSums(vf_geneCounts) 

vf_geneCounts_props %>% 
  kbl(caption = "VF Genes proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

VF Genes proportions in Chromsome and plasmids by Species

	Count_VFgenes_in_Chr	Count_VFgenes_in_Plasmid	Count_VFgenes_in_Chr_Plasmid
	100.00000	0.000000	0.00000
cfreundii	87.77778	12.222222	0.00000
cronobacter	100.00000	0.000000	0.00000
ecloacae	81.25000	18.750000	0.00000
ecoli	80.71066	4.060914	15.22843
kaerogenes	100.00000	0.000000	0.00000
koxytoca	100.00000	0.000000	0.00000
kpneumoniae	37.14286	28.571429	34.28571
senterica	100.00000	0.000000	0.00000

Plot2

VF Genes Percentages in Chromsome and plasmids by Species

vf_geneCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "VF Gene location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `VF Gene location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Virulence factor genes by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

Insertion Sequence (IS)

Table1

Percentages of Samples carrying Insertion Sequences in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe6 - Prokka Insertion Sequences 

Prokka_IS_df <- data.table::fread(file = "6_Prokka_InsertionSequences/CPE_Trans_Prokka_IS.tab", sep = "\t", header = TRUE)
#head(Prokka_IS_df)

colnames(Prokka_IS_df) <- c("Fasta", "Contig", "Fasta_Contig", "IS")

#----------------Step2: Combining plasClass dataframe with Prokka_IS dataframe for plotting Transposons

Prokka_IS_plasClass_df <- left_join(Prokka_IS_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,IS,Classification,Species_ContigClass)

#head(Prokka_IS_plasClass_df)

Prokka_IS_plasClass_df <- Prokka_IS_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # Prokka_IS_plasClass_df now has only 1198 assembly information

# Prokka_IS_plasClass_df %>%
#   select(Transposon,Species_ContigClass) %>%
#   table() %>% # frequency of unique transposons
#   kbl(caption = "Transposons and their counts in Chromosomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

customGreen0 = "#DeF7E9"
customRed = "#ff7f7f"
Prokka_IS_plasClass_df_total_col = Prokka_IS_plasClass_df %>% 
  select(Fasta.x,IS,Species_ContigClass) %>% 
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()


# percentages of IS (https://clarewest.github.io/blog/post/making-tables-shiny/)
Prokka_IS_plasClass_df %>%
  select(Fasta.x,IS,Species_ContigClass) %>%
  unique() %>%
  select(IS,Species_ContigClass) %>%
  count(IS, Species_ContigClass) %>%
  rename(Freq = n) %>%
  drop_na(Species_ContigClass) %>%
  mutate(Species_ContigClass2=Species_ContigClass) %>%
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>%
  left_join(Species_Counts, by = c("Species")) %>%
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>%
  select(IS,Species_ContigClass,SamplePrcnt_wGeneElement) %>%
  data.table::dcast(IS~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>%
  replace(is.na(.), 0) %>%
  mutate_if(is.numeric, round, digits=1) %>% 
  formattable(., align = c("l",rep("c", Prokka_IS_plasClass_df_total_col$n)), 
              list(
                `IS` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
                area(col = 1:Prokka_IS_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

Insertion Sequences and their unique counts in Chromsomes and Plasmids across Species

Prokka_IS_plasClass_df %>% 
  select(IS,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "Insertion Sequence and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Insertion Sequence and their unique counts in Chromsomes and Plasmids across Species

	-#Chromosome	-#Plasmid	cfreundii#Chromosome	cfreundii#Plasmid	cronobacter#Chromosome	cronobacter#Plasmid	ecloacae#Chromosome	ecloacae#Plasmid	ecoli#Chromosome	ecoli#Plasmid	kaerogenes#Chromosome	kaerogenes#Plasmid	koxytoca#Chromosome	koxytoca#Plasmid	kpneumoniae#Chromosome	kpneumoniae#Plasmid	senterica#Chromosome	senterica#Plasmid
IS100	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
IS1006	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
IS100kyp	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
IS10A	1	0	0	1	0	1	0	1	0	1	0	0	0	0	0	1	0	0
IS10R	1	1	0	1	0	0	1	1	1	1	1	1	0	0	1	1	0	0
IS1133	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
IS1203	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
IS1222	0	0	0	1	0	0	1	1	0	0	0	1	0	0	0	0	0	0
IS1230B	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0
IS1247	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
IS1271	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
IS1294	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS1326	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0
IS1351	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
IS1353	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
IS1394	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS1396	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
IS1397	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
IS1400	1	0	1	0	1	0	1	0	0	0	1	1	0	0	1	1	0	0
IS1414	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0
IS1489v1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS1491	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS15	0	0	0	1	0	0	0	1	1	1	0	1	0	1	1	1	0	0
IS150	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1541B	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS15DI	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
IS15DII	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS15DIV	1	1	1	1	0	1	1	1	1	1	1	1	0	1	1	1	0	0
IS1618	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
IS186A	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS186B	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1A	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
IS1B	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
IS1D	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0
IS1F	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1G	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS1H	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS1N	0	0	1	0	0	0	1	1	1	1	1	0	0	1	1	1	0	0
IS1S	0	0	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS1SD	0	0	1	1	0	0	1	0	1	1	0	1	0	1	1	1	0	0
IS1X1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0	0
IS1X2	1	1	1	1	0	1	1	1	1	1	0	1	1	1	1	1	0	0
IS1X3	1	1	1	1	0	1	1	1	1	1	0	0	1	1	1	1	0	0
IS1X4	1	1	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
IS2	0	0	1	1	0	0	1	1	1	1	0	0	1	0	1	1	0	0
IS200C	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS200F	0	0	1	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0
IS21	0	0	1	1	0	0	1	0	1	1	0	0	0	0	0	0	0	0
IS231L	1	0	1	0	0	0	1	0	1	0	0	0	1	0	1	0	0	0
IS285	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0
IS3	0	0	1	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
IS3000	0	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
IS30D	0	0	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
IS30H	1	0	1	1	0	0	1	1	0	0	0	0	0	0	0	0	0	0
IS3411	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
IS3F	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
IS3H	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
IS4	0	0	1	1	0	0	1	0	1	1	0	0	1	0	0	0	0	0
IS406	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
IS421	0	0	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
IS4321	0	1	1	1	0	0	1	1	0	1	0	0	0	0	1	1	0	0
IS4321R	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	0	0
IS4811	1	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0
IS5075	0	1	1	1	0	0	1	1	1	1	0	1	1	1	1	1	0	0
IS50R	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
IS5564	1	0	1	0	0	0	1	0	1	0	1	0	1	0	1	1	0	0
IS5708	1	1	1	1	0	0	1	1	1	1	0	0	0	1	0	0	0	0
IS5B	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0	0
IS5D	1	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
IS606	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
IS609	1	0	1	1	0	0	1	0	1	1	0	0	0	0	1	1	0	0
IS6100	1	1	0	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
IS621	0	0	0	0	0	0	1	1	1	1	0	0	0	0	1	0	0	0
IS629	1	0	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
IS630	0	0	1	0	0	0	1	0	1	1	1	1	0	0	1	0	0	0
IS679	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
IS682	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	1	0	0
IS884	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS903	0	1	1	1	0	1	1	1	1	1	0	1	1	1	1	1	0	0
IS903B	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	0	0
IS91	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
IS911	0	0	1	1	0	0	1	1	1	1	0	0	0	0	0	0	0	0
ISAav1	0	0	0	0	0	0	1	1	0	1	0	0	0	0	1	1	0	0
ISAba1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISAba125	0	1	0	1	0	1	1	1	1	1	0	1	0	1	0	1	0	1
ISAba14	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISAba33	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISAba42	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISAba43	1	1	1	0	0	0	0	1	1	1	0	1	0	0	1	1	0	0
ISAba8	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISAbe14	1	0	1	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISAcba1	0	0	0	1	0	0	1	0	1	1	0	0	1	0	1	1	0	0
ISAeca1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISAeme15	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISAeme19	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISAeme9	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISAfe13	0	1	1	1	0	1	1	1	0	1	0	1	0	1	1	1	0	1
ISAlde2	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISAlw16	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISApl1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISApu1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISApu2	0	1	1	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISArsp9	1	1	1	1	0	0	1	1	1	1	0	0	0	1	1	1	0	0
ISAs17	0	0	1	1	0	0	0	1	0	1	1	1	0	0	1	1	0	0
ISAs19	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISAs25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISAs29	0	0	1	1	0	0	1	1	0	0	0	0	0	0	1	1	0	0
ISAs9	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISAzs36	0	0	1	1	0	0	1	1	0	1	0	1	0	0	0	1	0	0
ISBcen15	0	0	1	0	0	0	1	0	1	0	0	1	0	0	1	1	0	0
ISBdi12	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISBli29	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISBli6	1	0	1	1	0	0	0	0	1	1	0	0	0	0	1	1	0	0
ISBmo1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISBmu20	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISBos1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISBrsa1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISBsp7	1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
ISBth5	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISCARN93	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISCco2	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISCco3	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISCep1	1	0	1	0	1	0	1	0	1	1	1	0	1	0	1	0	1	0
ISCfr1	1	1	1	1	0	1	1	1	1	1	0	0	0	1	1	1	0	0
ISCfr12	0	0	0	0	0	0	0	0	1	1	0	1	0	0	1	1	0	0
ISCfr13	0	0	1	0	0	0	1	1	0	0	1	0	0	0	1	1	0	0
ISCfr14	0	0	1	1	0	0	1	1	0	1	0	1	0	0	1	1	0	0
ISCfr26	0	0	1	1	0	0	1	1	0	1	0	0	0	0	0	1	0	0
ISCfr3	0	0	1	1	0	1	0	1	1	1	0	0	0	0	0	1	0	0
ISCfr4	1	0	1	1	0	0	0	0	1	0	1	1	0	0	1	1	0	0
ISCfr6	0	1	0	0	0	0	1	1	1	1	1	0	0	0	1	1	0	0
ISCfr8	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0
ISCgl1	1	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISCot1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	0
ISCro1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0
ISCro2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISCro3	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISCro4	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0
ISCro5	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISCro6	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISCte8	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISDsh3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISEae1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
ISEal1	1	0	1	1	0	0	1	1	0	0	0	1	1	1	1	1	0	0
ISEam1	1	0	1	1	0	0	1	1	1	0	1	0	0	0	1	1	0	0
ISEas1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISEc1	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc10	0	0	1	0	0	0	0	0	1	1	0	0	1	1	1	1	0	0
ISEc11	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISEc12	1	0	1	1	0	0	1	0	1	1	0	0	0	0	1	1	0	0
ISEc13	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISEc14	1	0	1	1	0	0	1	1	1	1	0	1	0	0	1	1	0	0
ISEc15	0	1	1	1	0	0	1	1	0	1	0	0	1	0	1	1	0	0
ISEc16	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc17	1	0	1	1	0	0	1	1	1	1	0	0	0	1	1	1	0	0
ISEc18	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc19	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc20	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc21	0	0	1	1	0	0	1	0	1	1	0	0	0	1	1	1	0	0
ISEc22	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0
ISEc23	1	1	0	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISEc24	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	0	0
ISEc25	1	0	1	1	0	0	1	1	0	1	0	0	0	0	1	1	0	0
ISEc26	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc27	0	0	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISEc29	0	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0	0
ISEc30	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc31	0	0	1	0	0	0	1	0	1	1	1	0	0	0	1	1	0	0
ISEc32	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
ISEc33	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	1
ISEc35	0	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISEc36	0	0	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
ISEc37	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
ISEc38	1	0	1	1	0	0	0	1	1	1	0	1	0	1	1	1	0	0
ISEc39	1	0	1	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0
ISEc40	0	1	1	1	0	0	0	0	1	0	0	0	1	1	1	1	0	0
ISEc41	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0
ISEc42	0	0	0	1	0	0	0	0	1	1	0	0	1	0	0	1	0	0
ISEc43	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc44	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc45	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc46	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISEc47	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISEc48	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc49	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEc52	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISEc53	1	0	1	0	0	0	1	0	1	1	1	0	0	0	1	0	0	0
ISEc59	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0
ISEc60	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISEc62	0	0	1	1	0	0	1	1	1	1	0	1	0	0	0	1	0	0
ISEc63	0	1	1	1	0	0	1	1	1	1	0	0	0	1	0	1	0	0
ISEc66	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc68	0	1	1	0	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISEc76	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc77	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISEc78	1	0	1	1	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISEc8	0	0	1	1	0	0	1	1	1	1	0	1	0	0	1	1	0	0
ISEc81	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISEc83	1	0	1	0	0	0	1	0	1	1	0	0	0	0	1	0	0	0
ISEc84	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISEc86	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEc9	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISEcB1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ISEch11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISEch12	1	0	1	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0
ISEch14	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEch2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEch6	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEcl1	0	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISEcl10	1	0	1	0	1	0	1	0	1	1	0	0	0	0	1	0	0	0
ISEcl11	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISEcl3	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISEcl6	0	0	1	0	1	0	1	1	1	0	0	1	1	1	1	1	0	0
ISEhe2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
ISEhe3	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	0
ISEhe4	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	1	0	0
ISEic2	0	0	1	0	1	0	0	1	1	1	0	0	0	0	1	1	0	0
ISEisp1	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0	0	0
ISErsp1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0
ISEsa1	0	1	1	1	0	0	1	1	1	0	0	0	0	1	1	1	0	0
ISEsa2	0	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISEsp1	0	0	1	1	1	0	1	1	1	1	0	0	1	1	1	1	0	0
ISGme8	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISHce1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISHgi14	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISHne2	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
ISHp1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISIde1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISKox1	0	0	0	1	0	0	1	1	1	0	1	0	0	0	1	1	0	0
ISKox2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISKox3	1	1	1	1	0	0	1	1	1	1	1	0	0	0	1	1	0	0
ISKpn1	1	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISKpn11	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
ISKpn12	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0
ISKpn14	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
ISKpn15	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISKpn18	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISKpn19	0	1	1	1	0	0	0	1	1	1	0	1	0	0	1	1	0	0
ISKpn2	0	0	1	0	0	0	1	0	1	0	0	0	1	0	1	1	0	0
ISKpn20	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
ISKpn21	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	0	0
ISKpn24	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISKpn25	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISKpn26	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	0	0
ISKpn27	0	1	1	1	0	1	0	1	1	1	0	1	0	1	1	1	0	0
ISKpn28	0	0	0	1	0	0	0	1	1	1	0	1	1	1	1	1	0	0
ISKpn3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISKpn31	1	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
ISKpn33	0	0	1	0	0	0	1	1	0	0	0	0	1	1	1	1	0	0
ISKpn34	1	0	1	1	0	0	1	1	0	1	0	0	0	0	1	1	0	0
ISKpn37	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISKpn38	0	1	1	1	0	0	1	1	1	1	0	1	1	1	1	1	0	0
ISKpn4	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISKpn40	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISKpn41	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISKpn42	0	0	1	0	0	0	0	1	1	1	0	0	0	0	1	1	0	0
ISKpn43	0	1	1	1	0	0	0	1	0	1	0	0	0	1	1	1	0	0
ISKpn47	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0
ISKpn49	1	0	1	1	0	0	1	1	1	0	0	0	0	1	1	1	0	0
ISKpn50	0	0	1	0	0	0	1	1	1	0	0	0	0	0	1	1	0	0
ISKpn53	1	0	1	0	1	0	1	0	1	1	1	1	0	0	1	1	0	0
ISKpn54	1	1	1	0	0	0	1	0	1	0	1	1	1	0	1	1	0	0
ISKpn6	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISKpn60	1	1	1	1	0	0	1	1	1	1	0	1	1	1	1	1	0	0
ISKpn66	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISKpn7	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISKpn8	1	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISKqu3	0	1	0	0	0	0	0	1	1	1	0	1	0	1	1	1	0	0
ISLad1	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
ISLad2	1	1	1	1	0	0	1	1	1	1	0	0	1	1	1	1	0	0
ISLad6	0	0	1	1	0	0	1	1	1	1	0	0	1	1	0	1	0	0
ISLead2	0	0	1	1	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISLead3	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISLpn8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISMaq2	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0
ISMasp6	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISMph1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISMtsp21	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0
ISNisp5	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0
ISNpu13	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPa16	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISPa21	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPa31	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISPa38	0	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
ISPa7	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISPa77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPa80	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ISPa83	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPa85	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPa95	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	1	0	0
ISPa97	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISPa99	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0
ISPan1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0
ISPcc1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPcc2	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPcc4	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPcc6	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISPeat2	1	0	0	1	0	0	1	1	0	0	0	0	0	0	0	1	0	0
ISPlge2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISPlge3	1	1	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0	0
ISPlge4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPlu15	1	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0
ISPlu22	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISPlu8	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPmi3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPpr10	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPps1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISPpu1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPpu12	0	0	1	1	0	0	1	0	1	1	0	0	0	0	0	1	0	0
ISPpu22	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPpu23	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISPpu30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPre2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPrre1	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISPrre10	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0
ISPrst3	1	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISPst2	0	0	1	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
ISPst3	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISPst4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPst8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISPst9	0	0	1	1	0	0	1	0	1	0	0	0	0	0	0	1	0	0
ISPsy30	0	0	0	0	0	0	1	1	1	1	0	0	1	0	0	1	0	0
ISPsy42	0	1	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0	0
ISPsy43	0	0	0	1	0	0	1	1	0	1	0	1	0	1	1	1	0	0
ISPye16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ISRaq1	1	1	1	1	1	1	1	1	0	1	0	0	1	0	1	1	0	0
ISRhba1	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0
ISRop1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1	1	0	0
ISRor2	1	0	1	1	1	0	0	1	1	0	1	0	1	1	1	1	1	0
ISRor3	1	0	1	1	1	0	0	1	1	0	1	1	1	1	1	1	0	0
ISRosp3	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISRso16	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0
ISRso21	1	0	1	0	1	0	1	0	1	0	1	0	0	0	1	0	0	0
ISSaen1	1	0	1	0	0	0	1	0	1	0	0	0	1	0	1	1	0	0
ISSav7	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0
ISSba1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSba14	0	1	1	0	0	0	1	1	1	1	0	1	0	0	0	1	0	0
ISSba3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSba8	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISSbo1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISSd1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSde4	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0
ISSde5	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSen1	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSen10	0	0	1	0	0	0	1	0	0	1	0	0	1	1	1	0	0	0
ISSen13	0	0	1	0	0	0	0	1	1	1	0	0	1	0	1	1	0	0
ISSen3	0	0	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISSen4	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	1
ISSen6	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0
ISSen7	0	0	1	1	0	0	0	1	1	0	0	0	1	1	1	1	0	0
ISSen8	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0
ISSen9	0	0	0	1	0	0	1	1	0	1	0	0	0	1	1	1	0	0
ISSfl1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0
ISSfl10	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSfl3	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSfl8	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISSfl9	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISSgsp1	0	0	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
ISShdy1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
ISShdy2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISShes11	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0
ISShes4	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISShfr8	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISShfr9	1	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0
ISShwo2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISSlo2	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISSm1	0	0	1	1	0	0	1	1	0	1	0	0	0	1	0	1	0	0
ISSm3	0	0	1	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0
ISSm4	1	0	1	1	1	0	1	1	1	1	1	0	0	0	1	1	1	0
ISSod16	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSod23	0	0	1	0	0	0	0	0	1	1	0	0	1	0	1	0	0	0
ISSod4	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISSod6	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSoEn2	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
ISSoEn3	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISSpe2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISSpr1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSpr2	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSpu11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISSpu2	0	0	0	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
ISSpu20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
ISSpu6	1	1	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0
ISSpu7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISSso4	1	1	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0	0
ISSsu9	1	1	1	1	1	0	1	1	1	1	1	1	1	0	1	1	1	0
ISStma11	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	0
ISSty2	0	0	0	0	0	0	1	0	0	0	0	1	1	0	1	1	0	0
ISSysp7	1	1	1	0	0	0	1	1	1	0	1	0	1	0	0	1	0	0
ISTfu1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISThi1	0	0	0	0	0	0	1	0	1	1	0	0	0	0	1	1	0	0
ISThsp9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISUnCu16	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISVch3	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0
ISVch4	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISVch9	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISVsa17	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISVsa3	0	0	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
ISWpi13	0	0	1	1	0	0	1	1	1	1	0	0	0	1	0	1	0	0
ISXc4	0	0	0	1	0	0	0	1	0	1	0	0	0	0	1	1	0	0
ISXne1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISYal1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISYen3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ISYpe1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISYps1	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISYps3	0	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	0	0
ISYps8	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISYru1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0	0
Tn2	1	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	0
Tn3	0	1	0	1	0	0	0	1	1	1	0	0	0	0	1	1	0	0
Tn4430	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn5393	0	1	0	1	0	1	1	1	1	1	0	0	0	1	1	1	0	0
Tn5403	0	1	1	1	0	1	1	1	1	1	0	1	0	1	1	1	0	1
TnAs1	1	1	1	1	0	0	1	1	1	1	0	1	0	1	1	1	0	0
TnAs2	0	1	1	1	0	0	1	1	1	1	0	1	1	0	1	1	0	0
TnAs3	1	1	1	1	0	0	1	1	1	1	0	0	0	0	1	1	0	0
TnEc1	1	0	0	1	0	0	1	1	1	1	1	1	0	1	1	1	0	0
TnShfr1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	1	0	0
TnXax1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Insertion Sequences UpsetR

#----------------Step3: UpsetR Plot VFGenes

IS_upsetR_df <- Prokka_IS_plasClass_df  %>% select(IS,Species_ContigClass)
#View(IS_upsetR_df)

IS_upsetR_lt <- split(IS_upsetR_df$IS, IS_upsetR_df$Species_ContigClass) 
IS_comb_mat = make_comb_mat(IS_upsetR_lt)

col_size = comb_size(IS_comb_mat)
row_size = set_size(IS_comb_mat)

IS_ht = UpSet(IS_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Insertion Sequences Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(IS_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(IS_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

IS_ht = draw(IS_ht)
#ht
col_od = column_order(IS_ht)
row_od = row_order(IS_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

Insertion Sequences proportions in Chromsome and plasmids by Species

Prokka_ISCounts <- Prokka_IS_plasClass_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,IS,ContigClass) %>% 
  unique() %>% 
  mutate(Species_IS=paste(Species, IS, sep = '#')) %>% 
  select(Species_IS,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#VFgenes") %>% 
  separate("Species#VFgenes", sep = "#", into = c("Species", "VFGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_IS_in_Chr_old=sum(Chromosome), Count_IS_in_Plasmid_old=sum(Plasmid), Count_IS_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_IS_in_Chr = Count_IS_in_Chr_old - Count_IS_in_Chr_Plasmid, 
         Count_IS_in_Plasmid = Count_IS_in_Plasmid_old - Count_IS_in_Chr_Plasmid) %>% 
  select(Species,Count_IS_in_Chr,Count_IS_in_Plasmid,Count_IS_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
Prokka_ISCounts_props <- Prokka_ISCounts*100/rowSums(Prokka_ISCounts) 

Prokka_ISCounts_props %>% 
  kbl(caption = "Insertion Sequences proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Insertion Sequences proportions in Chromsome and plasmids by Species

	Count_IS_in_Chr	Count_IS_in_Plasmid	Count_IS_in_Chr_Plasmid
	40.64516	29.67742	29.677419
cfreundii	32.28700	17.48879	50.224215
cronobacter	40.90909	56.81818	2.272727
ecloacae	28.51064	20.42553	51.063830
ecoli	24.00000	16.00000	60.000000
kaerogenes	22.77228	52.47525	24.752475
koxytoca	27.19298	42.98246	29.824561
kpneumoniae	12.72085	30.03534	57.243816
senterica	58.33333	41.66667	0.000000

Plot2

Insertion Sequences Percentages in Chromsome and plasmids by Species

Prokka_ISCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "Insertion Sequences location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `Insertion Sequences location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Insertion Sequences by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()